| | |
| | | 注:支持单条音频文件识别,也支持文件列表,列表为kaldi风格wav.scp:`wav_id wav_path` |
| | | |
| | | ### 非实时语音识别 |
| | | #### SenseVoice |
| | | ```python |
| | | from funasr import AutoModel |
| | | from funasr.utils.postprocess_utils import rich_transcription_postprocess |
| | | |
| | | model_dir = "iic/SenseVoiceSmall" |
| | | |
| | | model = AutoModel( |
| | | model=model_dir, |
| | | vad_model="fsmn-vad", |
| | | vad_kwargs={"max_single_segment_time": 30000}, |
| | | device="cuda:0", |
| | | ) |
| | | |
| | | # en |
| | | res = model.generate( |
| | | input=f"{model.model_path}/example/en.mp3", |
| | | cache={}, |
| | | language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" |
| | | use_itn=True, |
| | | batch_size_s=60, |
| | | merge_vad=True, # |
| | | merge_length_s=15, |
| | | ) |
| | | text = rich_transcription_postprocess(res[0]["text"]) |
| | | print(text) |
| | | ``` |
| | | 参数说明: |
| | | - `model_dir`:模型名称,或本地磁盘中的模型路径。 |
| | | - `vad_model`:表示开启VAD,VAD的作用是将长音频切割成短音频,此时推理耗时包括了VAD与SenseVoice总耗时,为链路耗时,如果需要单独测试SenseVoice模型耗时,可以关闭VAD模型。 |
| | | - `vad_kwargs`:表示VAD模型配置,`max_single_segment_time`: 表示`vad_model`最大切割音频时长, 单位是毫秒ms。 |
| | | - `use_itn`:输出结果中是否包含标点与逆文本正则化。 |
| | | - `batch_size_s` 表示采用动态batch,batch中总音频时长,单位为秒s。 |
| | | - `merge_vad`:是否将 vad 模型切割的短音频碎片合成,合并后长度为`merge_length_s`,单位为秒s。 |
| | | |
| | | #### Paraformer |
| | | ```python |
| | | from funasr import AutoModel |
| | | # paraformer-zh is a multi-functional asr model |